Increase MaxEpoch to decrease Std


This is a test with only the 1 dimensional DayOfTheWeek input. For the first day prediction, we run 50 different ANN trainings. They gave 50 slightly different forecasts for the first day of course. As we increased the maxEpoch, we decreased the randomness, the std.

The same in chart form:

– The dayOfTheWeek 1 dimensional case is a discretized input (1 dim, 5 bins)
Previously, we used maxEpoch=5 generally, sometimes maxEpoch=10. That was quite bad. It doesn’t modify the average of the prediction (our previous results), but it introduced unnecessary randomness we wanted to avoid.
Increasing maxEpoch is a good way of attacking randomness
even if we set maxEpoch to 400, most of the time, the learning exits much earlier (after about 10 epochs). The exit message is ‘Minimum gradient reached’
– for the combined (2 bins currDayChange, 2 bins EurChange) case, it usually stops after 7 epochs 90% of the case with the message ‘Minimum gradient reached’.
instead of using ensembles with 11 members, it may be less time consuming and better to increase the maxEpoch to 400 and use only ensemble with 5 members. If we increase maxEpoch, the computation takes longer only when needed, but after the ANN training converged, it will stop performing unnecessary epochs.
There is no point increasing maxEpoch from 400 to 1000. And although none of the forecasted numbers are the same after 8 digits, the std is the same
use 200 or 400 as maxEpoch for 1 dimensional input
use 200, if speed is an issue (you want backtest quicker)
the optimal maxEpoch needs to be defined a priori. Different target functions requires different maxEpoch. We expect a non discrete continuous case takes longer to learn. (so, it require greater maxEpoch)


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